A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification

Zhang, Jie; Zheng, Bowen; Gao, Ang; Feng, Xin; Liang, Dong; Long, Xiaojing

doi:10.1016/j.mri.2021.02.001

Cited by 113 publications

(54 citation statements)

References 15 publications

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“…Because the same NA-ADNI dataset was used, the prediction performance was basically comparable, despite differences such as the follow-up periods used to define sMCI and pMCI, input information, and evaluation methods (validation, k-fold cross-validation, and k-fold cross-validation and test). For the case of using only brain images as input, our model ( M2 in Supplementary Table 2 ), based on only T1-weighted MRI images (accuracy: 78%, AUC: 85%, sensitivity: 78%, specificity: 78%), performed better than a previous deep neural network (DNN) 8 (accuracy: 75%, AUC: not available [NA], sensitivity: 75%, specificity: 75%), equivalent to a DNN 9 that used a much easier task definition (accuracy: 79%, AUC: NA, sensitivity: 75%, specificity: 82%), but was inferior to a DNN 7 using MRI and PET images (accuracy: 83%, AUC: NA, sensitivity: 80%, specificity: 84%) and a DNN 10 using mixed groups of cognitively normal (CN) + sMCI and pSMI+AD groups for training (accuracy: 83%, AUC: 88%, sensitivity: 76%, specificity: 87%). For the case of using not only images, but also non-image information, the performance of our model ( M5 in Supplementary Table 2 ) (accuracy: 88%, AUC: 95%, sensitivity: 88%, specificity: 88%) was better than that of state-of-the-art models using SVM 13 , DNN 16 , and random forest 17 (accuracy: 85%–87%, AUC: 87%–90%).…”

Section: Resultsmentioning

confidence: 99%

A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

et al. 2022

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Alzheimer’s disease is a neurodegenerative disease that imposes a substantial financial burden on society. A number of machine learning studies have been conducted to predict the speed of its progression, which varies widely among different individuals, for recruiting fast progressors in future clinical trials. However, because the data in this field are very limited, two problems have yet to be solved: the first is that models built on limited data tend to induce overfitting and have low generalizability, and the second is that no cross-cohort evaluations have been done. Here, to suppress the overfitting caused by limited data, we propose a hybrid machine learning framework consisting of multiple convolutional neural networks that automatically extract image features from the point of view of brain segments, which are relevant to cognitive decline according to clinical findings, and a linear support vector classifier that uses extracted image features together with non-image information to make robust final predictions. The experimental results indicate that our model achieves superior performance (accuracy: 0.88, area under the curve [AUC]: 0.95) compared with other state-of-the-art methods. Moreover, our framework demonstrates high generalizability as a result of evaluations using a completely different cohort dataset (accuracy: 0.84, AUC: 0.91) collected from a different population than that used for training.

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Section: Resultsmentioning

confidence: 99%

A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

et al. 2022

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“…Fifth, 3D multiscale attention mechanism is introduced in dense unit. Zhang et al [ 73 ] used 3D densely connected convolutional neural network (CAM-CNN) to extract brain MRI multilevel features for classification of Alzheimer's disease and mild cognitive impairment, densely connected difference at different unit levels; 3D dense unit introduces attention mechanism to generate attention maps and sum transformed MRI hierarchical data into more compact high-level; model has high classification prediction accuracy, and classification performance is at the highest level.…”

Section: Development Of Densenetmentioning

confidence: 99%

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao

XinYu

et al. 2022

BioMed Research International

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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

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“…Experiments conducted in the study demonstrated that the bidirectional encoder with logistic regression outperformed the existing benchmarks. Zhang et al [ 100 ] proposed a densely connected CNN with attention mechanism for AD diagnosis using structural MR images. The densely connected CNN extracted multiple features from the input data, and the attention mechanism fused the features from different layers to transform them into complex features based on which final classification was performed.…”

Section: DL For Ad Diagnosismentioning

confidence: 99%

Deep Learning-Based Diagnosis of Alzheimer’s Disease

Saleem

Zahra

et al. 2022

JPM

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Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

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A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification

Cited by 113 publications

References 15 publications

A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

A high-generalizability machine learning framework for predicting the progression of Alzheimer’s disease using limited data

Dense Convolutional Network and Its Application in Medical Image Analysis

Deep Learning-Based Diagnosis of Alzheimer’s Disease

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